Abstract:
The Pine Processionary Moth (PPM) is considered the main defoliator of
pine trees and a menacing threat to various other perennial species including
oak and cedar. Given their negative secondary effects, spraying of pesticides has
been banned as a means for the eradication of PPM; instead, an individualized
approach is adopted, in which each nest is localized and destroyed. Detection of
PPM nests using optical sensing is challenging because of the changing outdoor
lighting conditions and the camouflaged appearance of moths in the underlying
foliage. In this thesis, a promising solution was proposed for nest detection by
fusing sensory data from a standard RGB camera on one hand and a thermal
camera on the other. The proposed detection system is built on a two-channeled
deep convolutional neural network (CNN), one for each spectrum of the collected
sensor data. Experiments performed in a pine forest report successful detection
rates with an average accuracy of 97%. Geo-localization is performed to report
back the position of the detected nests, within the scanned forest map, by means
of an estimation scheme that was designed for this purpose. The accuracy of the
proposed geo-localization scheme demonstrated an average localization accuracy
of a few centimeters. In summary, this thesis provides a novel scheme to detect
and localize PPM nests by creating a localized, tree-level scanning system that
can be deployed in urban areas.
